Basic multilayer perceptron architecture for solving linear regression problems. Network can be configured using the data file in the resource folder.
Example configuration:
0.01 % Learning rate
2 % Amount of inputs (e.g. input perceptrons in input layer)
8,1,3 % The remaining layers: hidden layer (8 perceptron), hidden layer(1 perceptron) and output layer (3 perceptrons)
4.0,-1.0,2.0 % One data sample containing 2 features and one class.
0.0,1.0,2.0
2.0,-2.0,-2.0
5.0,-1.0,3.0
1.0,-1.0,-1.0
2.0,2.0,6.0